LAUSR

Reinforcement of polymers with multiple inclusions of varying length scales and morphologies enable enhancement and tailorability of thermo-mechanical properties in resulting polymers. Computational material models can eliminate the trial-and-error approach of developing these hybrid reinforced polymers, enable prediction of interphase properties, and allow virtual exploration of design space. In this work, computational models, specifically representative volume elements were developed for acrylonitrile butadiene styrene polymer reinforced with nanoscale iron oxide particles and micro-scale short carbon fibers. These representative volume elements were used to predict the tensile modulus of resulting polymer nanocomposite with varying particle concentrations, orientations, interphases, and clustering to realistically replicate the actual material as observed in optical and electron microscopy. The interphase elastic modulus was obtained through established analytical formulations and incorporated into the representative volume elements by defining an interphase region around the reinforcements. The tensile modulus estimated using representative volume elements agreed well with the experiments, evidently showing that the effective tensile modulus of the polymer nanocomposite increased with increase in interphase thickness, aspect ratio, and particle content. Clustering was only observed in Fe3O4 nanoparticles but its size did not have any effect on the effective tensile modulus. The developed computational modeling framework and the resultant prediction of tensile modulus offers a design path which can be extended to other polymer nanocomposites containing multiple inclusions.